JupyterLite is an online notebook that runs entirely in the web browser. The Deepnote might be a good solution if you are looking for a cloud-based solution with a private workspace. They provide integrations with GitHub, PostgreSQL, BigQuery, Redshift, and others. They have many custom features like scheduling, converting notebook to application, visual chart creator to name a few. You can have private and public notebooks. The Deepnote offers real-time collaboration on notebooks. You can run notebooks on different size machines with GPU support. What is more, you can get access to paid plans for free if you are a student or a teacher. The free plan has 750 hours of computing. Their user interface is different from the original Jupyter Notebook but it looks really impressive (it’s my opinion). It is backed by Y-Combinator and well funded. The Deepnote is a startup working on an online notebook. This is a great solution if you want to learn machine learning and compete with others. Additionally, there is an option to install custom packages - it can be done in the cell running the command with ! (example !pip install mljar-supervised). The available environment comes with tons of already preinstalled data science packages. The notebook user interface is different from the original Jupyter Notebook. Public notebooks are great for sharing on Kaggle with other competitors. User can create private and public notebooks. The available machine can be with GPU support but for a limited time. The user needs to verify the account to get access to all features (like internet access). It provides a service for creating and running Python notebooks online. The is a website that hosts machine learning competitions. The service can be self-hosted after purchasing the commercial license. There is no access to GPU in the service. What is more, there are some great features available as chat or grading (very useful for teachers).ĬoCalc provides kernels for Python, Sage, R, Octave, and many more languages. You can’t install custom packages and there is no internet access from the notebook. The service provides free and paid plans. It offers the same look and feels as the original Jupyter Notebook. The CoCalc notebook is provided by SageMath company. The MyBinder is a good solution for researchers who want to share their findings with others. The environment will be created for you on notebook starts. You can define your own environment, but you need to define the packages in requirements.txt or environment.yml file. The launch of the notebook might be slow. The notebook can access only files that are already available online. It is an online service for running notebooks that are available from a public code repository (for example, your notebook needs to be in the public GitHub repo). You don’t need to signup if you already have an email account on Google. It is one of the most popular online Jupyter notebook because of its generous free plan. The link to Colab FAQ if you would like to check more details. There is internet access in the notebooks. The type of available devices can vary depending on time and usage. There is access to GPUs and TPUs on the free plan. If longer executions are needed, the paid plan should be considered. The notebook’s maximum computational run is 12 hours on the free plan. You can install your own packages, but after installation, you need to restart a kernel. It has a slightly different user interface than the original Jupyter Notebook. The Google Colab implementation is proprietary. Real-time collaboration is not available. You can also easily access files from Google Drive. The notebooks from Google Colab are saved in Google Drive. It offers free and paid tiers ( pricing website). The Google Colaboratory is also known as Google Colab. What is more, I will also show you two new alternative approaches for running notebooks online. I would like to show you some popular services that offer Jupyter Notebooks online. There are many services that allow you to create, edit and run Python notebooks with premade data science environment. The convenient alternative can be running notebooks in the cloud. You need to install Python and tons of packages to make it works. Setting up a local environment to run Jupyter Notebook can be cumbersome.
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